Technological advances depend on the study of specific materials, such as TiO₂ nanotubes that have a variety of applications in different industries due to their properties. These properties are directly related to the nanotubes, size, for example, with their length; hence, measuring this dimension accurately is important. Nowadays, length measurement is performed through semi-automatic functions on scanning electron microscopy images. Time-consuming image analysis, subjective and low-representative readings, and damaged samples are some disadvantages found in this process. This paper presents a proposal for predicting the average length of vertically aligned TiO₂ nanotubes using machine learning and ellipsometry because they can overcome the disadvantages mentioned. Different models of measurements of light reflection intensity and ellipsometric parameters predicted the length. The results of a model that showed a low prediction error using linear support vector machines for regression are reported.

中文翻译：

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机器学习预测垂直排列的TiO2纳米管的平均长度

技术的进步取决于对特定材料的研究，例如TiO ₂纳米管由于其特性而在不同行业中具有多种应用。这些性质与纳米管的大小直接相关，例如与长度有关。因此，准确测量此尺寸非常重要。如今，长度测量是通过半自动功能对扫描电子显微镜图像进行的。费时的图像分析，主观和低代表性的读数以及损坏的样本是此过程中的一些缺点。本文提出了预测垂直排列的TiO ₂平均长度的建议纳米管使用机器学习和椭圆偏光法，因为它们可以克服上述缺点。测量光反射强度和椭偏参数的不同模型预测了长度。报告了使用线性支持向量机进行回归的预测误差较低的模型的结果。